The modern communications era has brought about a tremendous expansion of wireline and wireless networks. Computer networks, television networks, and telephony networks are experiencing an unprecedented technological expansion, fueled by consumer demand. Wireless and mobile networking technologies have addressed related consumer demands, while providing more flexibility and immediacy of information transfer.
Current and future networking technologies continue to facilitate ease of information transfer and convenience to users. One area in which there is a demand to increase ease of information transfer relates to data processing services for data being communicated to/from a mobile terminal. The data processing services may be provided from a network server or other network device, from the mobile terminal such as, for example, a mobile telephone, a mobile television, a mobile gaming system, etc, or even from a combination of the mobile terminal and the network device. The data processing services may include transmission, reception, coding, decoding, storage, or other processing of, for example, image files, speech, video or audio signals, etc.
In current applications, a user of a mobile terminal who wishes to wirelessly communicate data comprising images, speech, audio, video, etc., which has been captured by the mobile terminal may experience delays in transmitting the data to the network device. The delays may result due to the relatively long transmission times required to transmit relatively large quantities of data (e.g., large image files captured by imaging devices on modern mobile terminals). Accordingly, compression techniques have been developed in order to reduce transmission times and storage requirements. However, compression techniques may introduce errors or reduce the quality of the compressed data when the data is decompressed tor rendering.
An exemplary field in which compression may introduce problems may be speech coding. In this regard, for example, in low bit rate speech coding, spectral information related to a speech signal may be considered to include two portions. A first portion, including a model of the human vocal tract, may be modeled using linear prediction and is referred to as the spectral envelope. The spectral envelope is typically considered the most important part of the spectral information. A second portion of the spectral information may include remaining parts of the spectral information which include detailed information that, while useful in adding to the speech quality, may not be necessary for clear comprehension of the speech. The second portion may be referred to as the residual spectrum or excitation spectrum. The residual spectrum typically has lower perceptual importance, but if the residual spectrum is neglected, speech quality usually suffers to some extent. Since residual information comprising the residual spectrum includes details that cannot be easily modeled by the spectral envelope, the amount of information to be quantized therein may be high.
Several frequency domain and time domain techniques have been developed for modeling and quantizing the residual spectrum. However, these techniques typically share common weaknesses with respect to providing modeling that may be considered too coarse for achieving high quality, or for requiring a large number of bits for quantizing the model parameters for relatively low bit rates. Moreover, conventional mechanisms have typically modeled and quantized the residual spectrum and the spectral envelope separately.
Other types of data may also include portions of the data to be compressed that are of unequal importance. Thus, given the above described problems, it may be advantageous to provide an improved data compression technique that may overcome at least some of the disadvantages described above to, for example, produce improved compression in low bit rate environments.